From 5c016127cb1567fe8bfa294c1568961b8e46bfd1 Mon Sep 17 00:00:00 2001
From: ItzCrazyKns <95534749+ItzCrazyKns@users.noreply.github.com>
Date: Thu, 20 Mar 2025 12:41:07 +0530
Subject: [PATCH] feat(package): bump version

---
 docs/API/SEARCH.md           | 4 ++--
 docs/architecture/WORKING.md | 2 +-
 package.json                 | 2 +-
 src/lib/config.ts            | 2 +-
 4 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/API/SEARCH.md b/docs/API/SEARCH.md
index 0169976..3007901 100644
--- a/docs/API/SEARCH.md
+++ b/docs/API/SEARCH.md
@@ -6,9 +6,9 @@ Perplexica’s Search API makes it easy to use our AI-powered search engine. You
 
 ## Endpoint
 
-### **POST** `http://localhost:3001/api/search`
+### **POST** `http://localhost:3000/api/search`
 
-**Note**: Replace `3001` with any other port if you've changed the default PORT
+**Note**: Replace `3000` with any other port if you've changed the default PORT
 
 ### Request
 
diff --git a/docs/architecture/WORKING.md b/docs/architecture/WORKING.md
index 75b20fd..6bad4f9 100644
--- a/docs/architecture/WORKING.md
+++ b/docs/architecture/WORKING.md
@@ -4,7 +4,7 @@ Curious about how Perplexica works? Don't worry, we'll cover it here. Before we
 
 We'll understand how Perplexica works by taking an example of a scenario where a user asks: "How does an A.C. work?". We'll break down the process into steps to make it easier to understand. The steps are as follows:
 
-1. The message is sent via WS to the backend server where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
+1. The message is sent to the `/api/chat` route where it invokes the chain. The chain will depend on your focus mode. For this example, let's assume we use the "webSearch" focus mode.
 2. The chain is now invoked; first, the message is passed to another chain where it first predicts (using the chat history and the question) whether there is a need for sources and searching the web. If there is, it will generate a query (in accordance with the chat history) for searching the web that we'll take up later. If not, the chain will end there, and then the answer generator chain, also known as the response generator, will be started.
 3. The query returned by the first chain is passed to SearXNG to search the web for information.
 4. After the information is retrieved, it is based on keyword-based search. We then convert the information into embeddings and the query as well, then we perform a similarity search to find the most relevant sources to answer the query.
diff --git a/package.json b/package.json
index 904e0b2..e2cf944 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "perplexica-frontend",
-  "version": "1.10.0-rc3",
+  "version": "1.10.0",
   "license": "MIT",
   "author": "ItzCrazyKns",
   "scripts": {
diff --git a/src/lib/config.ts b/src/lib/config.ts
index dac1ca5..ef99eed 100644
--- a/src/lib/config.ts
+++ b/src/lib/config.ts
@@ -59,7 +59,7 @@ export const getAnthropicApiKey = () => loadConfig().MODELS.ANTHROPIC.API_KEY;
 export const getGeminiApiKey = () => loadConfig().MODELS.GEMINI.API_KEY;
 
 export const getSearxngApiEndpoint = () =>
-  loadConfig().API_ENDPOINTS.SEARXNG || process.env.SEARXNG_API_URL;
+  process.env.SEARXNG_API_URL || loadConfig().API_ENDPOINTS.SEARXNG;
 
 export const getOllamaApiEndpoint = () => loadConfig().MODELS.OLLAMA.API_URL;